Enabling smart urban services with GPS trajectory data / Chao Chen, Daqing Zhang, Yasha Wang, Hongyu Huang.
Chen, Chao (Computer scientist); Zhang, Daqing.; Wang, Yasha.; Huang, Hongyu.
2021
G70.4
Available Online

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Title Enabling smart urban services with GPS trajectory data / Chao Chen, Daqing Zhang, Yasha Wang, Hongyu Huang.
Author Chen, Chao (Computer scientist)
ISBN 9789811601781 (electronic bk.)
981160178X (electronic bk.)
9811601771
9789811601774
DOI https://doi.org/10.1007/978-981-16-0178-1
Imprint Singapore : Springer, 2021.
Language English
Description 1 online resource (351 pages)
Other Standard Identifiers 10.1007/978-981-16-0178-1 doi
Call Number G70.4
Dewey Decimal Classification 910.285
Summary With the proliferation of GPS devices in daily life, trajectory data that records where and when people move is now readily available on a large scale. As one of the most typical representatives, it has now become widely recognized that taxi trajectory data provides rich opportunities to enable promising smart urban services. Yet, a considerable gap still exists between the raw data available, and the extraction of actionable intelligence. This gap poses fundamental challenges on how we can achieve such intelligence. These challenges include inaccuracy issues, large data volumes to process, and sparse GPS data, to name but a few. Moreover, the movements of taxis and the leaving trajectory data are the result of a complex interplay between several parties, including drivers, passengers, travellers, urban planners, etc. In this book, we present our latest findings on mining taxi GPS trajectory data to enable a number of smart urban services, and to bring us one step closer to the vision of smart mobility. Firstly, we focus on some fundamental issues in trajectory data mining and analytics, including data map-matching, data compression, and data protection. Secondly, driven by the real needs and the most common concerns of each party involved, we formulate each problem mathematically and propose novel data mining or machine learning methods to solve it. Extensive evaluations with real-world datasets are also provided, to demonstrate the effectiveness and efficiency of using trajectory data. Unlike other books, which deal with people and goods transportation separately, this book also extends smart urban services to goods transportation by introducing the idea of crowdshipping, i.e., recruiting taxis to make package deliveries on the basis of real-time information. Since people and goods are two essential components of smart cities, we feel this extension is bot logical and essential. Lastly, we discuss the most important scientific problems and open issues in mining GPS trajectory data.
Bibliography, etc. Note References.
Includes bibliographical references.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (SpringerLink, viewed April 22, 2021).
Added Author Zhang, Daqing.
Wang, Yasha.
Huang, Hongyu.
Available in Other Form Enabling Smart Urban Services with GPS Trajectory Data.
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
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Intro
Preface
Acknowledgments
Contents
Part I: Foundations
Chapter 1: Trajectory Data Map-matching
1.1 Introduction
1.2 Related Work
1.3 Main Concepts
1.4 The SD-Matching Algorithm
1.4.1 Algorithm Overview
1.4.2 Identifying Top-k Candidate Edges for a Given GPS Point
1.4.2.1 Spatial Probability
1.4.2.2 Direction Probability
1.4.3 Finding Paths Between Two Consecutive GPS Points
1.4.4 Refining Paths for a Given Raw Trajectory Segment
1.5 Evaluations
1.5.1 Data Preparation
1.5.2 Evaluation Criteria
1.5.3 Baseline Methods

1.5.4 Effectiveness Study
1.5.4.1 Varying l
1.5.4.2 Varying k
1.5.5 Efficiency Study
1.5.5.1 Varying l
1.5.5.2 Varying k
1.5.5.3 Time Cost at Different Stages
1.6 Conclusions and Future Work
References
Chapter 2: Trajectory Data Compression
2.1 Introduction
2.2 Related Work
2.2.1 Line-Simplification-Based Compression
2.2.2 Map-Matching-Based Compression
2.3 Basic Concepts and System Overview
2.3.1 Basic Concepts
2.3.2 System Overview
2.4 HCC Algorithm
2.4.1 Motivation
2.4.2 Algorithm Details
2.4.3 Trajectory Decompression

2.5 Evaluations of HCC
2.5.1 Baselines
2.5.2 Experimental Setup
2.5.3 Varying l
2.5.4 Boundary Choice of l
2.5.5 Quality of Response for When-and-Where Query
2.6 Evaluations of VTracer
2.6.1 Experimental Setup
2.6.2 Effectiveness Study
2.6.3 Efficiency Study
2.6.3.1 Time Cost
2.6.3.2 Energy Consumption
2.6.3.3 Memory Consumption
2.7 Conclusions and Future Work
References
Chapter 3: Trajectory Data Protection
3.1 Introduction
3.2 Related Work
3.3 Preliminary
3.4 Trajectory Protection Mechanism
3.5 Evaluations

3.6 Conclusions and Future Work
References
Part II: Enabling Smart Urban Services: Drivers
Chapter 4: Hunting or Waiting: Earning More by Understanding Taxi Service Strategies
4.1 Introduction
4.2 Related Work
4.3 Empirical Study
4.3.1 Extracting Individual Driverś Digital Traces
4.3.1.1 Shift Handover Event Detection
4.3.1.2 Statistical Study of Taxi Shift Handover Location and Time
4.3.2 Analyzing Individual Driverś Performance
4.3.2.1 Taxi Performance Quantification
4.3.2.2 Number of Passenger Delivery Trips

4.3.2.3 Popular Passenger Pickup and Drop-Off Areas
4.4 Taxi Strategy Formulation
4.4.1 Taxi Service Strategy Extraction
4.4.1.1 Passenger-Searching Strategies
4.4.1.2 Passenger-Delivery Strategies
4.4.1.3 Service-Area Preference
4.4.2 Driver-Strategy Matrix Construction
4.5 Understanding Taxi Service Strategies
4.5.1 Discovering Good Taxi Service Strategies
4.5.1.1 Passenger-Searching Strategies
4.5.1.2 Passenger-Delivery Strategies
4.5.1.3 Service-Area Preference
4.5.2 Performance Prediction Based on Historical Strategies
4.6 Conclusions and Future Work

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